Questions:

1. Characterize Emotion Dynamics
2. Heterogeneity between people?
3. Effectiveness of intervention?

Three Sessions:

1. Looking at data, descriptives, basic modeling (Jonas)
2. Modeling and model selection (Jerry)
3. Non-stationarity / trends and causality (Lourens)

… with Eiko chiming in here and there!

All analyses shown in this session are fully reproducible and available at http://github.com/jmbh/NSMDworkshopMay22.

Open data available from Rowland, Wenzel, and Kubiak (2020).

• Goal: study effect of mindfulness on affect network
• 125 undergraduate students
• 4 positive, 4 negative emotions
• 40 consecutive days
• six measures / day
• so, maximum of 240 measurements
• response scale: 0-100 analogue slider
• Two groups: Mindfulness intervention vs. Control

data <- readRDS("ESMdata.RDS")
head(data)

##   subj_id dayno beep group happy excited relaxed satisfied angry anxious
## 1       1     1    1     2    79      64      30        74    19       7
## 2       1     1    2     2    NA      NA      NA        NA    NA      NA
## 3       1     1    3     2    20      24      37        36    62      41
## 4       1     1    4     2    25      24      33        29    42      68
## 5       1     1    5     2    37      24      40        17    35      36
## 6       1     1    6     2    NA      NA      NA        NA    NA      NA
##   depressed sad
## 1         8  58
## 2        NA  NA
## 3        73  67
## 4        77  81
## 5        12  76
## 6        NA  NA

length(unique(data$subj_id))

## [1] 125

First check: Proportion of completed measurements across individuals:

Most missing sequences are only a few measurement occasions long

Most missing sequences are only a few measurement occasions long

• “Good” for trend-type analyses
• “Bad” for lagged-effects-type analyses

Time effects:

(from Sun, Rhemtulla, and Vazire (2021))

Missingness is likely caused by the characteristics of people we are studying.

(from Silvia et al. (2013))

There are also results on dynamic predictors of missingness, see Rintala et al. (2020) and Sun, Rhemtulla, and Vazire (2021).

head(data)

##   subj_id dayno beep group happy excited relaxed satisfied angry anxious
## 1       1     1    1     2    79      64      30        74    19       7
## 2       1     1    2     2    NA      NA      NA        NA    NA      NA
## 3       1     1    3     2    20      24      37        36    62      41
## 4       1     1    4     2    25      24      33        29    42      68
## 5       1     1    5     2    37      24      40        17    35      36
## 6       1     1    6     2    NA      NA      NA        NA    NA      NA
##   depressed sad
## 1         8  58
## 2        NA  NA
## 3        73  67
## 4        77  81
## 5        12  76
## 6        NA  NA

Heterogeneity: see PDF

Heterogeneity: see PDF

As always: means/SDs can be misleading if variables are not unimodal & symmetric.

Preprint: https://psyarxiv.com/qudr6

Variance captures only a small aspect of time evolution.

\(\text{RMSSD} = \sqrt{\frac{1}{N}\sum_t^{N} (X_t - X_{t-1})^2 }\) \(\;\;\;\;\;\;\;\;\;\;\) \(\text{AR} = \text{cor}(X_t, X_{t-1})\)

out <- lm(X[-1] ~ X[-length(X)])
out$coefficients

##   (Intercept) X[-length(X)] 
##    46.0438002     0.3542108

set.seed(1)
X_sim <- rep(NA, 240)
X_sim[1] <- X[1]
coefs <- out$coefficients
res_sd <- sd(residuals(out))
for(i in 2:240) X_sim[i] <- coefs[1] + coefs[2]*X_sim[i-1] + rnorm(1, 0, res_sd)

round(ints, 2) # Intercepts

## [1] 61.81 46.45 33.22 27.58

• Useful summary of the data (conditional autocorrelations and cross-correlations)
• But: Unlikely a (good approximation of a) generating mechanism

• How can we use statistical models if they are no plausible generating/mechanistic models?

An illustration of the issue:

Haslbeck, J. M., & Ryan, O. (2021). Recovering within-person dynamics from psychological time series. Multivariate Behavioral Research, 1-32. LINK

Some ideas on how to move forward:

Haslbeck, J., Ryan, O., Robinaugh, D. J., Waldorp, L. J., & Borsboom, D. (2021). Modeling psychopathology: From data models to formal theories. Psychological Methods. LINK

Missingness

• Many ways to analyze it
• Can be seen as data/behavior

Looking at Data

• Critical to do before modeling
• Many discoveries: Modality, skewness, low variance, huge heterogeneity, etc.

Descriptives

• Mean, SD, RMSSD, Skew, Modality
• Can provide powerful characterization of data

Basic Modeling

• AR, VAR
• Distinction: Descriptive & generative models